Road crashes are a leading cause of death and injury. One common place for these to happen is at junctions (intersections) controlled by traffic signals. 'Red-light cameras' are now widely used to identify drivers that jump ('run') red lights, who can then be prosecuted. This review looked for studies of their effectiveness in reducing the number of times that drivers drive through red lights and the number of crashes. Very little research has been done and much of it has not allowed for the statistical problems that occur when recording this kind of information. However, five studies in Australia, Singapore and the USA all found that use of red-light cameras cut the number of crashes in which there were injuries. In the best conducted of these studies, the reduction was nearly 30%. More research is needed to determine best practice for red-light camera programmes, including how camera sites are selected, signing policies, publicity programmes and penalties.
Red-light cameras are effective in reducing total casualty crashes. The evidence is less conclusive on total collisions, specific casualty collision types and violations, where reductions achieved could be explained by the play of chance. Most evaluations did not adjust for RTM or spillover, affecting their accuracy. Larger and better controlled studies are needed.
Road crashes are a prime cause of death and disability and red-light running is a common cause of crashes at signalised intersections. Red-light cameras are increasingly used to promote compliance with traffic signals. Manual enforcement methods are resource intensive and high risk, whereas red-light cameras can operate 24 hours a day and do not involve high-speed pursuits.
To quantify the impact of red-light cameras on the incidence and severity of road crashes and casualties, and the incidence of red-light violations.
We searched the following electronic databases: TRANSPORT (NTIS, TRIS, IRRD,TRANSDOC), Cochrane Injuries Group Specialised Register, Cochrane Controlled Trials Register, MEDLINE, EMBASE and the Australian Transport Index. We checked the reference lists of relevant papers and contacted research and advocacy organisations.
Randomised or quasi-controlled trials and controlled before-after studies of red-light cameras. For crash impact evaluation, the before and after periods each had to be at least one year in length. For violation studies, the after period had to occur at least one year after camera installation.
Two reviewers independently extracted data on study type, characteristics of camera and control areas, and data collection period. Before-after data were collected on number of crashes by severity, collision type, deaths and injuries, and red-light violations. Rate ratio was calculated for each study. Where there was more than one, rate ratios were pooled to give an overall estimate, using a generic inverse variance method and a random-effects model.
No randomised controlled trials were identified but 10 controlled before-after studies from Australia, Singapore and the USA met our inclusion criteria. We grouped them according to the extent to which they adjusted for regression to the mean (RTM) and spillover effects. Total casualty crashes: the only study that adjusted for both reported a rate ratio of 0.71 (95% CI to 0.55, 0.93); for three that partially adjusted for RTM but failed to consider spillover, rate ratio was 0.87 (95% CI to 0.77, 0.98); one that made no adjustments had a rate ratio of 0.80 (95% CI 0.58 to 1.12). Right-angle casualty crashes: rate ratio for two studies that partially addressed RTM was 0.76 (95% CI 0.54 to 1.07). Total crashes: the study addressing both RTM and spillover reported a rate ratio of 0.93 (95% CI 0.83 to 1.05); one study that partially addressed RTM had a rate ratio of 0.92 (95% CI 0.73 to 1.15); the pooled rate ratio from the five studies with no adjustments was 0.74 (95% CI 0.53 to 1.03). Red-light violations: one study found a rate ratio of 0.53 (95% CI 0.17 to 1.66).